Laptops are vulnerable to theft, greatly increasing the likelihood of exposing sensitive files. Unfortunately, storing data in a cryptographic file system does not fully address this problem. Such systems ask the user to imbue them with long-term authority for decryption, but that authority can be used by anyone who physically possesses the machine. Forcing the user to frequently reestablish his identity is intrusive, encouraging him to disable encryption.

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This paper describes some of the consumer-driven usability research conducted by NCR Self Service Strategic Solutions in the development of an understanding of usability and user acceptance of leadingedge biometrics verification techniques. We discuss biometric techniques in general and focus upon the usability phases and issues, associated with iris verification technology at the Automated Teller Machine (ATM) user interface. The paper concludes with a review of some of the major research issues encountered, and an outline of future work in the area.

How does a machine know who is using it? Currently, systems assume that the user typing now is the same person who supplied a password days ago. Such persistent authentication is inappropriate for mobile and ubiquitous systems, because associations between people and devices are fleeting. To address this, we propose transient authentication. In this model, a user wears a small hardware token that authenticates the user to other devices over a short-range, wireless link. This paper presents the four principles of transient authentication, our experience applying the model to a cryptographic file system, and our plans for extending the model to other services and applications.

As people become more connected electronically, the ability to achieve a highly accurate automatic personal identification system is more critical. Biometric technology is a way to achieve fast, user-friendly authentication with a high level of accuracy. This presentation will highlight some of the benefits and the few limits of using biometrics for authentication. Emerging applications, both within the government and industry, will be discussed. 1 Introduction The information age is quickly revolutionizing the way transactions are completed. Everyday actions are increasingly being handled electronically, instead of with pencil and paper or face to face. This growth in electronic transactions has resulted in a greater demand for fast and accurate automatic user identification and authentication. Traditionally, two major types of automatic personal identification approaches [1] have been used: knowledge-based and token-based. Knowledge-based approaches use "something you know" t...

For my family and and friends everywhere. ii ACKNOWLEDGEMENTS This dissertation would not have been possible without the help of a great number of people. Each offered ideas, guidance, criticism, and encouragement. I extend my greatest thanks to: My PhD advisor, Professor Brian Noble. Five years ago he was willing to take a chance and take me on as his first student. After struggling through my few years as an Electrical Engineer he was able to fully convert me to a Computer Scientist. He encouraged me in my pursuit of an academic career, something that is now a reality. My Master’s advisor at the University of Virginia, Jörg Liebeherr. Jörg originally inspired me to pursue research. Our work together got me to the University of Michigan and he continues to provide valuable career advice. Peter Honeyman, Paul Resnick, Mingyan Liu, and Pete Chen for serving as my com-mittee. They made many helpful suggestions at my proposal and afterwards.

Abstract: In this paper, we present a new scheme for iris recognition from focus-varying sequences of iris images. Most of the current state-of-the-art iris recognition systems use the highly focused iris images to obtain high accuracy. These systems does not recognize defocused iris images. They also take much focusing time to acquire the high quality images. Unlike the current iris recognition systems, our proposed method can correctly recognize the defocused iris images because the iris image sequences have more information than single still images. We also apply a feature extraction method using direct linear discriminant analysis on wavelet subband to extract discriminative low-dimensional feature vectors. Our experimental results show that defocused images may be correctly recognized if we use multifocus image sequences as gallery images for iris recognition. In addition, The proposed system preserves both high security and user convenience. Key–Words: Iris recognition, image sequences, single still image, focus-varying information, DLDA 1

this paper we describe a system to show some limited effects on a static toy-car model and present techniques that can be used in similar setups. Our focus is on creating apparent motion for animation